{
  "id": 18698463,
  "title": "4 jobs my local AI handles for free that I would never trust with a cloud model",
  "url": "https://urgent.news/2026/11/07/4-jobs-my-local-ai-handles-for-free-that-i-would-never-trust-with-a",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-07T11:00:20.000Z",
  "source": {
    "name": "XDA Developers",
    "slug": "xda-developers",
    "url": "https://www.xda-developers.com/4-jobs-my-local-ai-handles-for-free-that-i-would-never-trust-with-a-cloud-model/"
  },
  "original_language": "en",
  "account": "In recent years, I have become increasingly concerned with the privacy implications of sharing all of my data with cloud-based AI services. These services, such as AI chatbots and grammar-check tools, have access to significantly more user data than search engines did in the past, making them particularly vulnerable to privacy breaches. However, despite these concerns, AI remains an incredibly useful tool for a wide variety of tasks. To address these privacy concerns while still retaining the functionality of AI, I have started using local models on my own hardware.\n\nOne area where I have found local models particularly useful is in grammar and spell-checking. Many online grammar-check services have integrated AI, often without users realizing that they are opting in to train the model. This can be problematic, as these services are pervasive and can run as browser extensions in every text box you open. Moreover, these services can be considered a form of keylogger, as they have access to all of the user's typing, which can include sensitive information.\n\nI have taken to using an open-source tool called Writing Tools, which routes my text to Qwen3.6-35B-A3B on my RTX 4070 Ti Super. This allows me to proofread my emails without sending any sensitive information to a third-party server. Local LLMs also provide a solution for PC maintenance tasks, such as updating apps, which can be handled by agents and sub-agents running via Antigravity or Claude Code harnesses. These agents have the ability to handle tedious tasks while keeping the user's data on their own device.\n\nScreenshots are another area where I have found local models to be beneficial. Screenshots can inadvertently reveal personal details, such as email addresses or Wi-Fi network names, which could be uploaded to a cloud model if not handled properly. By using a local model, such as llama.cpp or Qwen3.6-35B-A3B, I can review screenshots without uploading any sensitive information.\n\nLastly, I have found local LLMs to be incredibly useful for financial tasks, such as reconciling bank statements. Bank statements often contain sensitive information, such as account numbers and transaction details, which should never be shared with a cloud server. Using a local model ensures that this information remains private and secure on my own device. Overall, local LLMs have allowed me to maintain my privacy while still benefiting from the functionality of AI, and I believe they are a responsible and cost-effective alternative to cloud-based services.",
  "summary": "Not all my information is meant to reach OpenAI's servers",
  "key_points": [
    "Local AI handles grammar and spell-checking without sharing sensitive data",
    "Local models manage PC maintenance tasks like app updates securely",
    "Screenshots and financial tasks protected by reviewing on local LLMs"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}